Infector characteristics exposed by spatial analysis of SARS-CoV-2 sequence and demographic data analysed at fine geographical scales

TL;DR

This study uses spatial analysis of SARS-CoV-2 sequences and demographic data with machine learning to identify how infection dynamics vary across socio-economic groups during lockdowns.

Contribution

It introduces a novel application of Random Forest models to analyze fine-scale genomic and demographic data for understanding infection drivers.

Findings

Individuals from deprived areas more likely infected during lockdown

Disadvantaged communities less likely to spread infection further

Genetic distance analysis reveals socio-economic disparities in transmission

Abstract

Characterising drivers of SARS-CoV-2 circulation is crucial for understanding COVID-19 because of the severity of control measures adopted during the pandemic. Whole genome sequence data augmented with demographic metadata provides the best opportunity to do this. We use Random Forest Decision Tree models to analyse a combination of over 4000 SARS-CoV2 sequences from a densely sampled, mixed urban and rural population (Tayside) in Scotland in the period from August 2020 to July 2021, with fine scale geographical and socio-demographic metadata. Comparing periods in versus out of "lockdown" restrictions, we show using genetic distance relationships that individuals from more deprived areas are more likely to get infected during lockdown but less likely to spread the infection further. As disadvantaged communities were the most affected by both COVID-19 and its restrictions, our finding has important implications for informing future approaches to control future pandemics driven by similar respiratory infections.